Poster "non-convex optimization" Papers
30 papers found
Attention layers provably solve single-location regression
Pierre Marion, Raphaël Berthier, Gérard Biau et al.
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
Andrew Lowy, Daogao Liu
Efficient Adaptive Federated Optimization
Su Hyeong Lee, Sidharth Sharma, Manzil Zaheer et al.
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Shiyuan Zuo, Xingrun Yan, Rongfei Fan et al.
Machine Unlearning via Simulated Oracle Matching
Kristian G Georgiev, Roy Rinberg, Sam Park et al.
Non-convex entropic mean-field optimization via Best Response flow
Razvan-Andrei Lascu, Mateusz Majka
Non-Convex Tensor Recovery from Tube-Wise Sensing
Tongle Wu, Ying Sun
Quantum Speedups for Minimax Optimization and Beyond
Chengchang Liu, Zongqi Wan, Institute of Computing Jialin Zhang et al.
Spike-timing-dependent Hebbian learning as noisy gradient descent
Niklas Dexheimer, Sascha Gaudlitz, Johannes Schmidt-Hieber
A Universal Class of Sharpness-Aware Minimization Algorithms
Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri et al.
Barrier Algorithms for Constrained Non-Convex Optimization
Pavel Dvurechenskii, Mathias Staudigl
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui et al.
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics
Haoyang Zheng, Hengrong Du, Qi Feng et al.
Fair Federated Learning via the Proportional Veto Core
Bhaskar Ray Chaudhury, Aniket Murhekar, Zhuowen Yuan et al.
Generalization Analysis of Stochastic Weight Averaging with General Sampling
Wang Peng, Li Shen, Zerui Tao et al.
High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails
Langqi Liu, Yibo Wang, Lijun Zhang
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
Andrew Lowy, Jonathan Ullman, Stephen Wright
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
Batiste Le Bars, Aurélien Bellet, Marc Tommasi et al.
Improving Computational Complexity in Statistical Models with Local Curvature Information
Pedram Akbarian, Tongzheng Ren, Jiacheng Zhuo et al.
Monotone, Bi-Lipschitz, and Polyak-Łojasiewicz Networks
Ruigang Wang, Krishnamurthy Dvijotham, Ian Manchester
Non-convex Stochastic Composite Optimization with Polyak Momentum
Yuan Gao, Anton Rodomanov, Sebastian Stich
On Convergence of Incremental Gradient for Non-convex Smooth Functions
Anastasiia Koloskova, Nikita Doikov, Sebastian Stich et al.
Random Scaling and Momentum for Non-smooth Non-convex Optimization
Qinzi Zhang, Ashok Cutkosky
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
Aleksandr Beznosikov, David Dobre, Gauthier Gidel
SILVER: Single-loop variance reduction and application to federated learning
Kazusato Oko, Shunta Akiyama, Denny Wu et al.
Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions
Nikita Doikov, Sebastian Stich, Martin Jaggi
Stochastic Optimization with Arbitrary Recurrent Data Sampling
William Powell, Hanbaek Lyu
Supervised Matrix Factorization: Local Landscape Analysis and Applications
Joowon Lee, Hanbaek Lyu, Weixin Yao
Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data
Haitong LIU, Qiang Li, Hoi To Wai
What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis
Waïss Azizian, Franck Iutzeler, Jérôme Malick et al.